Showing 5 of total 5 results (show query)
pecanproject
PEcAn.priors:PEcAn Functions Used to Estimate Priors from Data
Functions to estimate priors from data.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.95 score 13 scripts 6 dependents
bioc
distinct:distinct: a method for differential analyses via hierarchical permutation tests
distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.
Maintained by Simone Tiberi. Last updated 5 months ago.
geneticsrnaseqsequencingdifferentialexpressiongeneexpressionmultiplecomparisonsoftwaretranscriptionstatisticalmethodvisualizationsinglecellflowcytometrygenetargetopenblascpp
11 stars 6.35 score 34 scripts 1 dependentsbioc
autonomics:Unified Statistical Modeling of Omics Data
This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.
Maintained by Aditya Bhagwat. Last updated 2 months ago.
softwaredataimportpreprocessingdimensionreductionprincipalcomponentregressiondifferentialexpressiongenesetenrichmenttranscriptomicstranscriptiongeneexpressionrnaseqmicroarrayproteomicsmetabolomicsmassspectrometry
5.95 score 5 scriptsbioc
EGAD:Extending guilt by association by degree
The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods.
Maintained by Sara Ballouz. Last updated 5 months ago.
softwarefunctionalgenomicssystemsbiologygenepredictionfunctionalpredictionnetworkenrichmentgraphandnetworknetwork
4.92 score 83 scriptsbioc
PRONE:The PROteomics Normalization Evaluator
High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.
Maintained by Lis Arend. Last updated 9 days ago.
proteomicspreprocessingnormalizationdifferentialexpressionvisualizationdata-analysisevaluation
2 stars 4.41 score 9 scripts